Developing Transportation Engineering and Planning Metrics for Rural Volunteer Driver Programs
Bibliographic record
Abstract
Car-based volunteer driver programs have emerged as important providers of transportation in many rural and low-density locations in which automobile dependence is high and transit access is negligible. The degree of this importance is not known, yet it can be expected that demand for these services will increase commensurate with the transportation needs of the aging population. The challenge remains that there is limited technical guidance for existing and new programs to anticipate and respond to the anticipated increases in travel demand from an aging population. It is also unclear how these programs can fit into transportation engineering and planning. This paper presents aggregate results of travel data collected for one year using a uniform data reporting approach by seven car-based volunteer driver programs in New Brunswick, Canada. Users 65 years and older accounted for 49–65% of members, and programs on average made 2.0 stops (trip end) per drive (trip chain), drove 39 km per stop, 61 km per drive, and had an occupancy of 1.5 riders per drive. A total of 88% of drives had 3 or fewer stops, and one drive was cancelled for every 10–11 offered. A total of 49.3% of all stops (excluding home) were for health purposes, with 20.7% for education/work. Seasonal, daily, and hourly trends were observed. Volunteers in groups with <100 riders provided more hours/drive but fewer hours/year than those in larger groups. These data can provide a basis for a more sophisticated approach to program planning and could be useful in forecasting for future demand.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.006 | 0.005 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".